Who is this presentation for?

Level

What you'll learn

Understand the most common problems ML organizations encounter as they grow

Learn how large organizations have led the way with in-house systems

Explore the pros and cons of build versus buy, best practices for automating model deployment and management, and automation pitfalls to avoid

Description

You constructed data warehouses and filled your data lake. You hired data scientists and trained amazing models. But when it was time to make those models work, you found your tech stack and DevOps processes really didn’t work for ML.

Models require specialized hardware, produce spiky compute cycles, and are written in different languages than the models and applications that consume them. That all adds up to a mess of manual labor, broken deployments, and spiraling costs, and it means your ML investments are generating losses.

Here’s the good news: there’s a path forward. ML leaders such as Google, Uber, and Facebook have led the way, building AI-specific model deployment and management systems—and they’re working. But what does that mean for the rest of us?

Diego Oppenheimer draws upon his work with thousands of developers across hundreds of organizations to discuss the tools and processes every business needs to automate model deployment and management so they can optimize model performance, control compute costs, maintain governance, and keep data scientists doing data science.

Diego Oppenheimer

Algorithmia

Diego Oppenheimer is the founder and CEO of Algorithmia. An entrepreneur and product developer with extensive background in all things data, Diego has designed, managed, and shipped some of Microsoft’s most used data analysis products, including Excel, Power Pivot, SQL Server, and Power BI. Diego holds a bachelor’s degree in information systems and a master’s degree in business intelligence and data analytics from Carnegie Mellon University.

Brendan Collins

Algorithmia

Brendan Collins is a Solutions Engineer working to implement machine learning infrastructure for Algorithmia’s largest enterprise customers. Previously, he held a similar position at Synology. He has worked in financial enterprise infrastructure for more than 10 years, with groups ranging in size from the largest financial institutions in the world to community banks. Brendan has a true passion for helping enterprises use machine learning and data science to solve cutting edge problems.

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